An organic spiking artificial neuron with excitatory and inhibitory synapses: towards soft and flexible organic neuromorphic processing
Source abstract
Abstract
Artificial neurons are key components of neuromorphic systems, which aim to emulate the structure and functions of biological neural networks for efficient, brain-like computation. However, most artificial neurons rely on rigid, silicon-based technologies that are poorly suited for integration with soft structures, such as soft robots or biological organisms. We report the first organic spiking neuron with excitatory and inhibitory synapses, constructed from complementary organic field-effect transistors and capacitors, integrated on the same flexible substrate. The circuit emulates key neural functions including signal integration, frequency modulation, coincidence detection, and tunable synaptic weights. The synapses demonstrate excitatory and inhibitory time constants of 60 and 280 ms, respectively. The neuron exhibits linear response properties, with output firing rates in the range 0–60 Hz. We showcase the neuron’s ability to interact with the environment, by embedding it in a light-control feedback loop that adjusts luminance based on ambient light intensity.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (80)
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